import logging
import duckdb
import pandas as pd
import polars as pl
import pyarrow as pa
import re
from coded_flows.types import Union, List, DataFrame, ArrowTable, Str

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


def _coalesce(*values):
    return next((v for v in values if v is not None), None)


def _sanitize_identifier(identifier):
    """
    Sanitize SQL identifier by escaping special characters.
    Handles double quotes and other problematic characters.
    """
    return identifier.replace('"', '""')


def _is_numeric_type(duckdb_type):
    """
    Check if a DuckDB type is numeric.
    """
    type_lower = duckdb_type.lower()
    return any(
        (
            t in type_lower
            for t in [
                "tinyint",
                "smallint",
                "integer",
                "bigint",
                "int",
                "float",
                "double",
                "real",
                "decimal",
                "numeric",
            ]
        )
    )


def impute_missing_values(
    data: Union[DataFrame, ArrowTable],
    columns: List = None,
    regex_pattern: Str = None,
    impute_method: Str = None,
    options=None,
) -> Union[DataFrame, ArrowTable]:
    brick_display_name = "Impute Missing Values"
    options = options or {}
    verbose = options.get("verbose", True)
    columns = _coalesce(columns, options.get("columns", []))
    regex_pattern = _coalesce(regex_pattern, options.get("regex_pattern", ""))
    fill_all_columns = len(columns) == 0
    impute_method = _coalesce(impute_method, options.get("impute_method", "mean"))
    output_format = options.get("output_format", "pandas")
    safe_mode = options.get("safe_mode", False)
    result = None
    conn = None
    if not isinstance(columns, list) or not all((isinstance(c, str) for c in columns)):
        verbose and logger.error(
            f"[{brick_display_name}] Invalid columns format! Expected a list."
        )
        raise ValueError("Columns must be provided as a list!")
    if impute_method not in ["mean", "median", "mode"]:
        verbose and logger.error(
            f"[{brick_display_name}] Invalid imputation method: '{impute_method}'. Must be 'mean', 'median', or 'mode'."
        )
        raise ValueError(
            f"Invalid imputation method: '{impute_method}'. Must be 'mean', 'median', or 'mode'."
        )
    try:
        impute_mode = None
        if regex_pattern:
            impute_mode = "regex_pattern"
        elif fill_all_columns:
            impute_mode = "all_columns"
        else:
            impute_mode = "column_list"
        verbose and logger.info(
            f"[{brick_display_name}] Detected mode: '{impute_mode}'. Starting imputation with method: '{impute_method}'."
        )
        data_type = None
        if isinstance(data, pd.DataFrame):
            data_type = "pandas"
        elif isinstance(data, pl.DataFrame):
            data_type = "polars"
        elif isinstance(data, (pa.Table, pa.lib.Table)):
            data_type = "arrow"
        if data_type is None:
            verbose and logger.error(
                f"[{brick_display_name}] Input data must be a pandas DataFrame, Polars DataFrame, or Arrow Table"
            )
            raise ValueError(
                "Input data must be a pandas DataFrame, Polars DataFrame, or Arrow Table"
            )
        verbose and logger.info(
            f"[{brick_display_name}] Detected input format: {data_type}."
        )
        conn = duckdb.connect(":memory:")
        conn.register("input_table", data)
        column_info = conn.execute("DESCRIBE input_table").fetchall()
        all_columns = {col[0]: col[1] for col in column_info}
        verbose and logger.info(
            f"[{brick_display_name}] Total columns in data: {len(all_columns)}."
        )
        columns_to_impute = []
        if impute_mode == "column_list":
            if not safe_mode:
                missing_columns = [col for col in columns if col not in all_columns]
                if missing_columns:
                    verbose and logger.error(
                        f"[{brick_display_name}] Columns not found in data: {missing_columns}"
                    )
                    raise ValueError(f"Columns not found in data: {missing_columns}")
            columns_to_impute = [col for col in columns if col in all_columns]
            skipped = len(columns) - len(columns_to_impute)
            if safe_mode and skipped > 0:
                skipped_cols = [col for col in columns if col not in all_columns]
                verbose and logger.warning(
                    f"[{brick_display_name}] Safe mode: Skipped {skipped} non-existent columns: {skipped_cols}"
                )
            verbose and logger.info(
                f"[{brick_display_name}] Imputing {len(columns_to_impute)} column(s): {columns_to_impute}."
            )
        elif impute_mode == "regex_pattern":
            try:
                pattern = re.compile(regex_pattern)
                columns_to_impute = [
                    col for col in all_columns.keys() if pattern.search(col)
                ]
                if not columns_to_impute:
                    verbose and logger.warning(
                        f"[{brick_display_name}] No columns matched regex pattern '{regex_pattern}'. Returning data unchanged."
                    )
                verbose and logger.info(
                    f"[{brick_display_name}] Regex pattern '{regex_pattern}' matched {len(columns_to_impute)} columns: {columns_to_impute}."
                )
            except re.error as e:
                verbose and logger.error(
                    f"[{brick_display_name}] Invalid regex pattern."
                )
                raise ValueError(f"Invalid regex pattern!")
        elif impute_mode == "all_columns":
            columns_to_impute = [
                col for col in all_columns.keys() if _is_numeric_type(all_columns[col])
            ]
            verbose and logger.info(
                f"[{brick_display_name}] Imputing all numeric columns: {len(columns_to_impute)} columns."
            )
        imputation_values = {}
        for col in columns_to_impute:
            col_type = all_columns[col]
            if not _is_numeric_type(col_type):
                if safe_mode:
                    verbose and logger.warning(
                        f"[{brick_display_name}] Safe mode: Skipping non-numeric column '{col}' (type: {col_type})."
                    )
                    continue
                else:
                    verbose and logger.error(
                        f"[{brick_display_name}] Column '{col}' is not numeric (type: {col_type})."
                    )
                    raise ValueError(
                        f"Column '{col}' is not numeric (type: {col_type}). Cannot impute missing values."
                    )
            sanitized_col = _sanitize_identifier(col)
            try:
                if impute_method == "mean":
                    query = f'SELECT AVG("{sanitized_col}") as impute_value FROM input_table'
                    impute_value = conn.execute(query).fetchone()[0]
                elif impute_method == "median":
                    query = f'SELECT PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY "{sanitized_col}") as impute_value FROM input_table WHERE "{sanitized_col}" IS NOT NULL'
                    impute_value = conn.execute(query).fetchone()[0]
                elif impute_method == "mode":
                    query = f'\n                        SELECT "{sanitized_col}" as impute_value \n                        FROM input_table \n                        WHERE "{sanitized_col}" IS NOT NULL \n                        GROUP BY "{sanitized_col}" \n                        ORDER BY COUNT(*) DESC \n                        LIMIT 1\n                    '
                    result_row = conn.execute(query).fetchone()
                    impute_value = result_row[0] if result_row else None
                if impute_value is None:
                    if safe_mode:
                        verbose and logger.warning(
                            f"[{brick_display_name}] Safe mode: Column '{col}' has no non-null values. Skipping imputation."
                        )
                        continue
                    else:
                        verbose and logger.error(
                            f"[{brick_display_name}] Column '{col}' has no non-null values to calculate {impute_method}."
                        )
                        raise ValueError(
                            f"Column '{col}' has no non-null values to calculate {impute_method}."
                        )
                imputation_values[col] = impute_value
                verbose and logger.info(
                    f"[{brick_display_name}] Column '{col}': calculated {impute_method} = {impute_value}."
                )
            except Exception as e:
                if safe_mode:
                    verbose and logger.warning(
                        f"[{brick_display_name}] Safe mode: Error calculating {impute_method} for column '{col}': {str(e)}"
                    )
                    continue
                else:
                    verbose and logger.error(
                        f"[{brick_display_name}] Error calculating {impute_method} for column '{col}'."
                    )
                    raise
        if len(imputation_values) == 0:
            verbose and logger.warning(
                f"[{brick_display_name}] No columns were imputed. Returning data unchanged."
            )
            result = data
        else:
            select_parts = []
            imputed_count = 0
            for col in all_columns.keys():
                sanitized_col = _sanitize_identifier(col)
                if col in imputation_values:
                    impute_value = imputation_values[col]
                    select_parts.append(
                        f'COALESCE("{sanitized_col}", {impute_value}) AS "{sanitized_col}"'
                    )
                    imputed_count += 1
                    verbose and logger.info(
                        f"[{brick_display_name}] Column '{col}': imputing NULL values with {impute_value}."
                    )
                else:
                    select_parts.append(f'"{sanitized_col}"')
            select_clause = ", ".join(select_parts)
            query = f"SELECT {select_clause} FROM input_table"
            verbose and logger.info(
                f"[{brick_display_name}] Executing query to impute missing values."
            )
            if output_format == "pandas":
                result = conn.execute(query).df()
                verbose and logger.info(
                    f"[{brick_display_name}] Converted result to pandas DataFrame."
                )
            elif output_format == "polars":
                result = conn.execute(query).pl()
                verbose and logger.info(
                    f"[{brick_display_name}] Converted result to Polars DataFrame."
                )
            elif output_format == "arrow":
                result = conn.execute(query).fetch_arrow_table()
                verbose and logger.info(
                    f"[{brick_display_name}] Converted result to Arrow Table."
                )
            else:
                verbose and logger.error(
                    f"[{brick_display_name}] Unsupported output format: {output_format}"
                )
                raise ValueError(f"Unsupported output format: {output_format}")
            verbose and logger.info(
                f"[{brick_display_name}] Imputation completed successfully. Imputed {imputed_count} column(s) using {impute_method}."
            )
    except Exception as e:
        verbose and logger.error(
            f"[{brick_display_name}] Error during imputation operation: {str(e)}"
        )
        raise
    finally:
        if conn is not None:
            conn.close()
    return result